Overview

Dataset statistics

Number of variables29
Number of observations148891
Missing cells0
Missing cells (%)0.0%
Duplicate rows456
Duplicate rows (%)0.3%
Total size in memory34.1 MiB
Average record size in memory240.0 B

Variable types

Numeric25
DateTime2
Categorical2

Alerts

Action has constant value "Sale" Constant
Dataset has 456 (0.3%) duplicate rowsDuplicates
trait_type has a high cardinality: 302 distinct values High cardinality
blockNumber is highly correlated with collectionTokenId and 2 other fieldsHigh correlation
collectionTokenId is highly correlated with blockNumber and 1 other fieldsHigh correlation
gasPrice is highly correlated with Active market wallets and 1 other fieldsHigh correlation
gas is highly correlated with blockNumber and 1 other fieldsHigh correlation
ETH_CLOSE_PRICE is highly correlated with INTEREST_SCORE_ARTBLOCKS and 5 other fieldsHigh correlation
INTEREST_SCORE_ARTBLOCKS is highly correlated with ETH_CLOSE_PRICE and 11 other fieldsHigh correlation
Number of sales is highly correlated with ETH_CLOSE_PRICE and 10 other fieldsHigh correlation
Sales USD is highly correlated with ETH_CLOSE_PRICE and 11 other fieldsHigh correlation
Active market wallets is highly correlated with gasPrice and 6 other fieldsHigh correlation
Unique buyers is highly correlated with ETH_CLOSE_PRICE and 6 other fieldsHigh correlation
Unique sellers is highly correlated with gasPrice and 6 other fieldsHigh correlation
Active market wallets collection is highly correlated with INTEREST_SCORE_ARTBLOCKS and 8 other fieldsHigh correlation
Unique buyers collection is highly correlated with INTEREST_SCORE_ARTBLOCKS and 7 other fieldsHigh correlation
Unique sellers collection is highly correlated with INTEREST_SCORE_ARTBLOCKS and 7 other fieldsHigh correlation
tokens_minted is highly correlated with Active market wallets collection and 1 other fieldsHigh correlation
tokens_available is highly correlated with blockNumber and 2 other fieldsHigh correlation
Transactions collection ETH is highly correlated with INTEREST_SCORE_ARTBLOCKS and 7 other fieldsHigh correlation
Number of Transactions collection is highly correlated with INTEREST_SCORE_ARTBLOCKS and 7 other fieldsHigh correlation
Transactions collection USD is highly correlated with INTEREST_SCORE_ARTBLOCKS and 8 other fieldsHigh correlation
rarity_trait_value is highly correlated with rarity_trait_typeHigh correlation
rarity_trait_type is highly correlated with rarity_trait_valueHigh correlation
blockNumber is highly correlated with collectionTokenId and 1 other fieldsHigh correlation
collectionTokenId is highly correlated with blockNumber and 1 other fieldsHigh correlation
ETH_CLOSE_PRICE is highly correlated with Number of sales and 3 other fieldsHigh correlation
INTEREST_SCORE_ARTBLOCKS is highly correlated with Number of sales and 10 other fieldsHigh correlation
Number of sales is highly correlated with ETH_CLOSE_PRICE and 7 other fieldsHigh correlation
Sales USD is highly correlated with INTEREST_SCORE_ARTBLOCKS and 10 other fieldsHigh correlation
Active market wallets is highly correlated with ETH_CLOSE_PRICE and 5 other fieldsHigh correlation
Unique buyers is highly correlated with ETH_CLOSE_PRICE and 5 other fieldsHigh correlation
Unique sellers is highly correlated with ETH_CLOSE_PRICE and 5 other fieldsHigh correlation
Active market wallets collection is highly correlated with INTEREST_SCORE_ARTBLOCKS and 7 other fieldsHigh correlation
Unique buyers collection is highly correlated with INTEREST_SCORE_ARTBLOCKS and 7 other fieldsHigh correlation
Unique sellers collection is highly correlated with INTEREST_SCORE_ARTBLOCKS and 6 other fieldsHigh correlation
tokens_minted is highly correlated with Active market wallets collection and 1 other fieldsHigh correlation
tokens_available is highly correlated with blockNumber and 1 other fieldsHigh correlation
Transactions collection ETH is highly correlated with INTEREST_SCORE_ARTBLOCKS and 7 other fieldsHigh correlation
Number of Transactions collection is highly correlated with INTEREST_SCORE_ARTBLOCKS and 6 other fieldsHigh correlation
Transactions collection USD is highly correlated with INTEREST_SCORE_ARTBLOCKS and 7 other fieldsHigh correlation
rarity_trait_value is highly correlated with rarity_trait_typeHigh correlation
rarity_trait_type is highly correlated with rarity_trait_valueHigh correlation
blockNumber is highly correlated with collectionTokenId and 1 other fieldsHigh correlation
collectionTokenId is highly correlated with blockNumber and 1 other fieldsHigh correlation
ETH_CLOSE_PRICE is highly correlated with Active market wallets and 1 other fieldsHigh correlation
INTEREST_SCORE_ARTBLOCKS is highly correlated with Number of sales and 3 other fieldsHigh correlation
Number of sales is highly correlated with INTEREST_SCORE_ARTBLOCKS and 4 other fieldsHigh correlation
Sales USD is highly correlated with INTEREST_SCORE_ARTBLOCKS and 6 other fieldsHigh correlation
Active market wallets is highly correlated with ETH_CLOSE_PRICE and 4 other fieldsHigh correlation
Unique buyers is highly correlated with Number of sales and 3 other fieldsHigh correlation
Unique sellers is highly correlated with ETH_CLOSE_PRICE and 3 other fieldsHigh correlation
Active market wallets collection is highly correlated with Unique buyers collection and 4 other fieldsHigh correlation
Unique buyers collection is highly correlated with Active market wallets collection and 5 other fieldsHigh correlation
Unique sellers collection is highly correlated with Active market wallets collection and 4 other fieldsHigh correlation
tokens_minted is highly correlated with Unique buyers collectionHigh correlation
tokens_available is highly correlated with blockNumber and 1 other fieldsHigh correlation
Transactions collection ETH is highly correlated with INTEREST_SCORE_ARTBLOCKS and 6 other fieldsHigh correlation
Number of Transactions collection is highly correlated with Active market wallets collection and 4 other fieldsHigh correlation
Transactions collection USD is highly correlated with INTEREST_SCORE_ARTBLOCKS and 7 other fieldsHigh correlation
rarity_trait_value is highly correlated with rarity_trait_typeHigh correlation
rarity_trait_type is highly correlated with rarity_trait_valueHigh correlation
blockNumber is highly correlated with collectionTokenId and 16 other fieldsHigh correlation
collectionTokenId is highly correlated with blockNumber and 9 other fieldsHigh correlation
ETH_CLOSE_PRICE is highly correlated with blockNumber and 15 other fieldsHigh correlation
ETH_TRADED_VOLUME is highly correlated with blockNumber and 7 other fieldsHigh correlation
INTEREST_SCORE_NFT is highly correlated with blockNumber and 12 other fieldsHigh correlation
INTEREST_SCORE_ARTBLOCKS is highly correlated with blockNumber and 16 other fieldsHigh correlation
Number of sales is highly correlated with blockNumber and 17 other fieldsHigh correlation
Sales USD is highly correlated with blockNumber and 14 other fieldsHigh correlation
Active market wallets is highly correlated with blockNumber and 16 other fieldsHigh correlation
Unique buyers is highly correlated with blockNumber and 15 other fieldsHigh correlation
Unique sellers is highly correlated with blockNumber and 16 other fieldsHigh correlation
Active market wallets collection is highly correlated with blockNumber and 15 other fieldsHigh correlation
Unique buyers collection is highly correlated with blockNumber and 15 other fieldsHigh correlation
Unique sellers collection is highly correlated with blockNumber and 14 other fieldsHigh correlation
tokens_minted is highly correlated with Number of sales and 8 other fieldsHigh correlation
tokens_available is highly correlated with blockNumber and 17 other fieldsHigh correlation
Transactions collection ETH is highly correlated with blockNumber and 15 other fieldsHigh correlation
Number of Transactions collection is highly correlated with blockNumber and 15 other fieldsHigh correlation
Transactions collection USD is highly correlated with blockNumber and 13 other fieldsHigh correlation
rarity_trait_value is highly correlated with collectionTokenId and 1 other fieldsHigh correlation
rarity_trait_type is highly correlated with collectionTokenId and 1 other fieldsHigh correlation
price is highly skewed (γ1 = 67.90066963) Skewed
number_trait_values has 7874 (5.3%) zeros Zeros

Reproduction

Analysis started2022-07-24 12:19:40.742194
Analysis finished2022-07-24 12:21:03.249022
Duration1 minute and 22.51 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

blockNumber
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct116666
Distinct (%)78.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13407316.25
Minimum11445035
Maximum15053170
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-07-24T13:21:03.308109image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum11445035
5-th percentile12446634
Q112968899
median13125799
Q313871756
95-th percentile14795817.5
Maximum15053170
Range3608135
Interquartile range (IQR)902857

Descriptive statistics

Standard deviation713224.1818
Coefficient of variation (CV)0.05319664045
Kurtosis-0.09602623798
Mean13407316.25
Median Absolute Deviation (MAD)278568
Skewness0.5111746623
Sum1.996228724 × 1012
Variance5.086887336 × 1011
MonotonicityNot monotonic
2022-07-24T13:21:03.385828image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1473074047
 
< 0.1%
1472730646
 
< 0.1%
1308249743
 
< 0.1%
1499121640
 
< 0.1%
1472620437
 
< 0.1%
1473290734
 
< 0.1%
1450422833
 
< 0.1%
1473128233
 
< 0.1%
1166137730
 
< 0.1%
1391093130
 
< 0.1%
Other values (116656)148518
99.7%
ValueCountFrequency (%)
114450351
< 0.1%
114452211
< 0.1%
114452271
< 0.1%
114453741
< 0.1%
114453751
< 0.1%
114453841
< 0.1%
114457251
< 0.1%
114457701
< 0.1%
114458021
< 0.1%
114458791
< 0.1%
ValueCountFrequency (%)
150531701
< 0.1%
150531621
< 0.1%
150531181
< 0.1%
150529241
< 0.1%
150528831
< 0.1%
150527361
< 0.1%
150526971
< 0.1%
150524401
< 0.1%
150524211
< 0.1%
150524171
< 0.1%
Distinct116682
Distinct (%)78.4%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
Minimum2020-12-13 13:46:48+00:00
Maximum2022-06-30 23:47:57+00:00
2022-07-24T13:21:03.462047image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-24T13:21:03.539606image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

collectionTokenId
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct95023
Distinct (%)63.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean132921561.8
Minimum3000003
Maximum328000398
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-07-24T13:21:03.617372image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum3000003
5-th percentile16000602
Q173000474
median121000770
Q3178000861
95-th percentile282000917
Maximum328000398
Range325000395
Interquartile range (IQR)105000387

Descriptive statistics

Standard deviation79580248.71
Coefficient of variation (CV)0.5987008251
Kurtosis-0.595186153
Mean132921561.8
Median Absolute Deviation (MAD)52000006
Skewness0.3797183831
Sum1.979082425 × 1013
Variance6.333015984 × 1015
MonotonicityNot monotonic
2022-07-24T13:21:03.694227image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
165000678268
 
0.2%
203000252259
 
0.2%
20300017316
 
< 0.1%
20300029413
 
< 0.1%
28100019412
 
< 0.1%
2900061111
 
< 0.1%
400040310
 
< 0.1%
3500014510
 
< 0.1%
40001179
 
< 0.1%
30003319
 
< 0.1%
Other values (95013)148274
99.6%
ValueCountFrequency (%)
30000031
 
< 0.1%
30000051
 
< 0.1%
30000065
< 0.1%
30000101
 
< 0.1%
30000132
 
< 0.1%
30000141
 
< 0.1%
30000172
 
< 0.1%
30000201
 
< 0.1%
30000221
 
< 0.1%
30000231
 
< 0.1%
ValueCountFrequency (%)
3280003981
< 0.1%
3280003961
< 0.1%
3280003951
< 0.1%
3280003941
< 0.1%
3280003901
< 0.1%
3280003891
< 0.1%
3280003831
< 0.1%
3280003771
< 0.1%
3280003751
< 0.1%
3280003741
< 0.1%

price
Real number (ℝ≥0)

SKEWED

Distinct7504
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.210530542
Minimum1 × 10-18
Maximum2100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-07-24T13:21:03.774397image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1 × 10-18
5-th percentile0.069
Q10.21
median0.55
Q31.734322313
95-th percentile7.5
Maximum2100
Range2100
Interquartile range (IQR)1.524322313

Descriptive statistics

Standard deviation12.79594898
Coefficient of variation (CV)5.788632521
Kurtosis8413.60882
Mean2.210530542
Median Absolute Deviation (MAD)0.421
Skewness67.90066963
Sum329128.1029
Variance163.7363104
MonotonicityNot monotonic
2022-07-24T13:21:03.847805image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.53052
 
2.0%
0.23015
 
2.0%
12786
 
1.9%
0.252767
 
1.9%
0.32557
 
1.7%
0.12492
 
1.7%
0.152395
 
1.6%
0.42220
 
1.5%
0.351847
 
1.2%
1.51798
 
1.2%
Other values (7494)123962
83.3%
ValueCountFrequency (%)
1 × 10-182
 
< 0.1%
1 × 10-61
 
< 0.1%
2.5 × 10-64
 
< 0.1%
7.142857143 × 10-614
< 0.1%
9.090909091 × 10-611
< 0.1%
1 × 10-51
 
< 0.1%
1.111111111 × 10-59
< 0.1%
1.25 × 10-516
< 0.1%
2 × 10-510
< 0.1%
2.5 × 10-58
< 0.1%
ValueCountFrequency (%)
21001
 
< 0.1%
18001
 
< 0.1%
10001
 
< 0.1%
9501
 
< 0.1%
7771
 
< 0.1%
7501
 
< 0.1%
7001
 
< 0.1%
6501
 
< 0.1%
5004
< 0.1%
4601
 
< 0.1%

gasPrice
Real number (ℝ≥0)

HIGH CORRELATION

Distinct82004
Distinct (%)55.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.685528828 × 10-8
Minimum0
Maximum8.316549733 × 10-6
Zeros12
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-07-24T13:21:03.924538image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2 × 10-8
Q13.8 × 10-8
median6.144180024 × 10-8
Q39.784798101 × 10-8
95-th percentile1.723519049 × 10-7
Maximum8.316549733 × 10-6
Range8.316549733 × 10-6
Interquartile range (IQR)5.984798101 × 10-8

Descriptive statistics

Standard deviation8.367418185 × 10-8
Coefficient of variation (CV)1.088723804
Kurtosis2188.648085
Mean7.685528828 × 10-8
Median Absolute Deviation (MAD)2.744180024 × 10-8
Skewness0
Sum0.01144306073
Variance7.001368708 × 10-15
MonotonicityNot monotonic
2022-07-24T13:21:03.999422image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.3 × 10-81261
 
0.8%
3 × 10-8997
 
0.7%
4 × 10-8975
 
0.7%
2.5 × 10-8908
 
0.6%
4.5 × 10-8840
 
0.6%
5 × 10-8836
 
0.6%
3.5 × 10-8831
 
0.6%
3.6 × 10-8814
 
0.5%
2.7 × 10-8805
 
0.5%
2.8 × 10-8798
 
0.5%
Other values (81994)139826
93.9%
ValueCountFrequency (%)
012
< 0.1%
4 × 10-91
 
< 0.1%
4.4 × 10-91
 
< 0.1%
5 × 10-916
< 0.1%
6 × 10-921
< 0.1%
7 × 10-924
< 0.1%
7.1 × 10-92
 
< 0.1%
7.5 × 10-91
 
< 0.1%
7.5611 × 10-91
 
< 0.1%
7.7 × 10-92
 
< 0.1%
ValueCountFrequency (%)
8.316549733 × 10-61
< 0.1%
8 × 10-61
< 0.1%
7.17740449 × 10-61
< 0.1%
6.760002156 × 10-61
< 0.1%
4.2 × 10-61
< 0.1%
4.16 × 10-61
< 0.1%
4.115229279 × 10-61
< 0.1%
4.08 × 10-62
< 0.1%
4.063538645 × 10-61
< 0.1%
4 × 10-61
< 0.1%

gas
Real number (ℝ≥0)

HIGH CORRELATION

Distinct8552
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.702373598 × 10-13
Minimum1.33735 × 10-13
Maximum1.2 × 10-11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-07-24T13:21:04.074509image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.33735 × 10-13
5-th percentile2.24225 × 10-13
Q12.51257 × 10-13
median3.02426 × 10-13
Q33.2464 × 10-13
95-th percentile3.67764 × 10-13
Maximum1.2 × 10-11
Range1.1866265 × 10-11
Interquartile range (IQR)7.3383 × 10-14

Descriptive statistics

Standard deviation5.454391522 × 10-13
Coefficient of variation (CV)1.473214785
Kurtosis0
Mean3.702373598 × 10-13
Median Absolute Deviation (MAD)2.6936 × 10-14
Skewness0
Sum5.512501073 × 10-8
Variance2.975038687 × 10-25
MonotonicityNot monotonic
2022-07-24T13:21:04.156907image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.3495 × 10-134850
 
3.3%
3.0241 × 10-134338
 
2.9%
3.10836 × 10-132728
 
1.8%
3.02478 × 10-132664
 
1.8%
2.34925 × 10-132444
 
1.6%
3.10768 × 10-132254
 
1.5%
2.88606 × 10-132040
 
1.4%
3.02379 × 10-131907
 
1.3%
2.24172 × 10-131741
 
1.2%
2.88538 × 10-131649
 
1.1%
Other values (8542)122276
82.1%
ValueCountFrequency (%)
1.33735 × 10-131
 
< 0.1%
1.77254 × 10-131
 
< 0.1%
1.79272 × 10-131
 
< 0.1%
1.8079 × 10-131
 
< 0.1%
1.80814 × 10-132
< 0.1%
1.80838 × 10-132
< 0.1%
1.81344 × 10-134
< 0.1%
1.81356 × 10-133
< 0.1%
1.81368 × 10-134
< 0.1%
1.8138 × 10-134
< 0.1%
ValueCountFrequency (%)
1.2 × 10-118
 
< 0.1%
1.0423119 × 10-1146
< 0.1%
1.0117848 × 10-1147
< 0.1%
1 × 10-114
 
< 0.1%
8.165973 × 10-1222
< 0.1%
7.58809 × 10-1234
< 0.1%
7.549304 × 10-1237
< 0.1%
7.387141 × 10-1233
< 0.1%
6.618088 × 10-1225
< 0.1%
6.525938 × 10-1233
< 0.1%

DATE
Date

Distinct561
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
Minimum2020-12-13 00:00:00
Maximum2022-06-30 00:00:00
2022-07-24T13:21:04.232230image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-24T13:21:04.309592image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ETH_CLOSE_PRICE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct561
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2963.775591
Minimum583.7146
Maximum4812.087402
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-07-24T13:21:04.386865image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum583.7146
5-th percentile1744.243408
Q12534.481689
median3106.671387
Q33319.257324
95-th percentile4269.73291
Maximum4812.087402
Range4228.372802
Interquartile range (IQR)784.775635

Descriptive statistics

Standard deviation764.6882602
Coefficient of variation (CV)0.2580115251
Kurtosis-0.06542014499
Mean2963.775591
Median Absolute Deviation (MAD)467.372071
Skewness-0.21886097
Sum441279511.5
Variance584748.1352
MonotonicityNot monotonic
2022-07-24T13:21:04.461776image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3319.2573245221
 
3.5%
3242.1154794190
 
2.8%
3172.4562993883
 
2.6%
2561.8520513060
 
2.1%
3226.0839842566
 
1.7%
3224.3742682244
 
1.5%
3100.3254392126
 
1.4%
2610.153322082
 
1.4%
2636.0930182051
 
1.4%
3270.600831996
 
1.3%
Other values (551)119472
80.2%
ValueCountFrequency (%)
583.71467
 
< 0.1%
586.0111696
 
< 0.1%
589.35559119
< 0.1%
589.66320819
< 0.1%
609.8178712
 
< 0.1%
611.6071787
 
< 0.1%
634.8541871
 
< 0.1%
635.8358151
 
< 0.1%
636.1818247
 
< 0.1%
638.2908337
 
< 0.1%
ValueCountFrequency (%)
4812.087402615
0.4%
4735.068848230
 
0.2%
4730.384277152
 
0.1%
4667.115234133
 
0.1%
4651.460449157
 
0.1%
4636.174316112
 
0.1%
4631.479004307
0.2%
4626.358887269
0.2%
4620.55468890
 
0.1%
4607.19384865
 
< 0.1%

ETH_TRADED_VOLUME
Real number (ℝ≥0)

HIGH CORRELATION

Distinct561
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.024556866 × 1010
Minimum6532996574
Maximum8.448291278 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-07-24T13:21:04.538355image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum6532996574
5-th percentile1.216455217 × 1010
Q11.598327846 × 1010
median1.930692448 × 1010
Q32.272155295 × 1010
95-th percentile3.132900054 × 1010
Maximum8.448291278 × 1010
Range7.79499162 × 1010
Interquartile range (IQR)6738274488

Descriptive statistics

Standard deviation6477404809
Coefficient of variation (CV)0.3199418559
Kurtosis8.691174234
Mean2.024556866 × 1010
Median Absolute Deviation (MAD)3389828928
Skewness1.877553231
Sum3.014382963 × 1015
Variance4.195677306 × 1019
MonotonicityNot monotonic
2022-07-24T13:21:04.613184image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.051111051 × 10105221
 
3.5%
1.598327846 × 10104190
 
2.8%
2.013102891 × 10103883
 
2.6%
2.269798706 × 10103060
 
2.1%
1.811397763 × 10102566
 
1.7%
1.930692448 × 10102244
 
1.5%
1.740566812 × 10102126
 
1.4%
2.21627541 × 10102082
 
1.4%
1.336927637 × 10102051
 
1.4%
1.8489602 × 10101996
 
1.3%
Other values (551)119472
80.2%
ValueCountFrequency (%)
6532996574118
0.1%
807236839659
 
< 0.1%
81258371026
 
< 0.1%
8546822406127
0.1%
863200037997
0.1%
8677951273112
0.1%
8766710365205
0.1%
885038593795
0.1%
887297660776
 
0.1%
8876420740151
0.1%
ValueCountFrequency (%)
8.448291278 × 101048
 
< 0.1%
7.839821454 × 101017
 
< 0.1%
6.902338218 × 10104
 
< 0.1%
6.761082668 × 101054
< 0.1%
6.269178901 × 10105
 
< 0.1%
6.240204516 × 101017
 
< 0.1%
6.07336303 × 10103
 
< 0.1%
5.694598576 × 10101
 
< 0.1%
5.600572198 × 101020
 
< 0.1%
5.541393392 × 1010129
0.1%

INTEREST_SCORE_NFT
Real number (ℝ≥0)

HIGH CORRELATION

Distinct45
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.83864371
Minimum10.5
Maximum74.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-07-24T13:21:04.688049image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum10.5
5-th percentile19
Q126
median28
Q334
95-th percentile44
Maximum74.5
Range64
Interquartile range (IQR)8

Descriptive statistics

Standard deviation8.643091892
Coefficient of variation (CV)0.2802682237
Kurtosis6.45035728
Mean30.83864371
Median Absolute Deviation (MAD)5
Skewness1.836996528
Sum4591596.5
Variance74.70303745
MonotonicityNot monotonic
2022-07-24T13:21:04.759842image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
2832519
21.8%
3412999
 
8.7%
4410086
 
6.8%
34.59922
 
6.7%
30.56202
 
4.2%
235882
 
4.0%
195220
 
3.5%
304517
 
3.0%
26.54342
 
2.9%
263922
 
2.6%
Other values (35)53280
35.8%
ValueCountFrequency (%)
10.587
 
0.1%
13.5705
 
0.5%
15.566
 
< 0.1%
171809
 
1.2%
195220
3.5%
20711
 
0.5%
20.51102
 
0.7%
21943
 
0.6%
21.52042
 
1.4%
222492
1.7%
ValueCountFrequency (%)
74.51772
 
1.2%
58.5182
 
0.1%
56364
 
0.2%
502772
 
1.9%
48.5407
 
0.3%
4410086
6.8%
422987
 
2.0%
41890
 
0.6%
40877
 
0.6%
39.5236
 
0.2%

INTEREST_SCORE_ARTBLOCKS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct42
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.88607102
Minimum18
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-07-24T13:21:04.834766image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile23
Q133
median47
Q366
95-th percentile100
Maximum100
Range82
Interquartile range (IQR)33

Descriptive statistics

Standard deviation24.61787516
Coefficient of variation (CV)0.4568504382
Kurtosis-0.7043313726
Mean53.88607102
Median Absolute Deviation (MAD)16
Skewness0.6815154772
Sum8023151
Variance606.0397772
MonotonicityNot monotonic
2022-07-24T13:21:04.905363image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
10021079
 
14.2%
4110555
 
7.1%
318809
 
5.9%
668485
 
5.7%
596993
 
4.7%
856773
 
4.5%
376546
 
4.4%
486286
 
4.2%
345345
 
3.6%
335337
 
3.6%
Other values (32)62683
42.1%
ValueCountFrequency (%)
183382
2.3%
2066
 
< 0.1%
211141
 
0.8%
222825
1.9%
23943
 
0.6%
24569
 
0.4%
26961
 
0.6%
271036
 
0.7%
281075
 
0.7%
294537
3.0%
ValueCountFrequency (%)
10021079
14.2%
856773
 
4.5%
764332
 
2.9%
731489
 
1.0%
671273
 
0.9%
668485
5.7%
643269
 
2.2%
612564
 
1.7%
602432
 
1.6%
596993
 
4.7%

Number of sales
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct559
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110135.8069
Minimum1056
Maximum209445
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-07-24T13:21:04.981048image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1056
5-th percentile11986
Q156876
median114371
Q3160885
95-th percentile182170
Maximum209445
Range208389
Interquartile range (IQR)104009

Descriptive statistics

Standard deviation56453.7668
Coefficient of variation (CV)0.5125832222
Kurtosis-1.161294944
Mean110135.8069
Median Absolute Deviation (MAD)48370
Skewness-0.3022991104
Sum1.639823043 × 1010
Variance3187027785
MonotonicityNot monotonic
2022-07-24T13:21:05.055317image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1426965221
 
3.5%
1426364190
 
2.8%
1621473883
 
2.6%
1084033060
 
2.1%
1724712566
 
1.7%
1629442244
 
1.5%
1821702126
 
1.4%
1143712082
 
1.4%
540382051
 
1.4%
2094451996
 
1.3%
Other values (549)119472
80.2%
ValueCountFrequency (%)
105634
< 0.1%
121319
< 0.1%
13023
 
< 0.1%
13191
 
< 0.1%
14131
 
< 0.1%
14834
 
< 0.1%
148726
< 0.1%
15376
 
< 0.1%
16113
 
< 0.1%
161915
< 0.1%
ValueCountFrequency (%)
2094451996
1.3%
20411997
 
0.1%
203000664
 
0.4%
194178146
 
0.1%
193872261
 
0.2%
190298468
 
0.3%
188702358
 
0.2%
186469303
 
0.2%
185701215
 
0.1%
185039207
 
0.1%

Sales USD
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct561
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110555334.5
Minimum186430.27
Maximum811137361.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-07-24T13:21:05.130712image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum186430.27
5-th percentile10156666.53
Q152319544.16
median98449709.11
Q3138840985.7
95-th percentile283666632.5
Maximum811137361.1
Range810950930.9
Interquartile range (IQR)86521441.53

Descriptive statistics

Standard deviation83543544.38
Coefficient of variation (CV)0.7556717614
Kurtosis8.81496842
Mean110555334.5
Median Absolute Deviation (MAD)40766602.68
Skewness1.996328746
Sum1.646069431 × 1013
Variance6.979523808 × 1015
MonotonicityNot monotonic
2022-07-24T13:21:05.209864image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
283666632.55221
 
3.5%
139216311.84190
 
2.8%
226528538.53883
 
2.6%
113820819.83060
 
2.1%
145508035.72566
 
1.7%
259680798.32244
 
1.5%
172773106.12126
 
1.4%
62301633.332082
 
1.4%
94068223.432051
 
1.4%
248109050.91996
 
1.3%
Other values (551)119472
80.2%
ValueCountFrequency (%)
186430.2719
< 0.1%
226524.456
 
< 0.1%
231478.637
 
< 0.1%
255937.956
 
< 0.1%
257635.016
 
< 0.1%
275656.8625
< 0.1%
291449.943
 
< 0.1%
293737.9726
< 0.1%
311377.0222
< 0.1%
314797.477
 
< 0.1%
ValueCountFrequency (%)
811137361.1232
 
0.2%
427569989.5995
 
0.7%
378413570.81884
 
1.3%
289436178.5161
 
0.1%
283666632.55221
3.5%
259680798.32244
1.5%
248109050.91996
 
1.3%
236092309.2160
 
0.1%
226528538.53883
2.6%
212449466.9205
 
0.1%

Active market wallets
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct554
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52178.38875
Minimum1037
Maximum118220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-07-24T13:21:05.288629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1037
5-th percentile6667
Q134672
median60382
Q369043
95-th percentile84685
Maximum118220
Range117183
Interquartile range (IQR)34371

Descriptive statistics

Standard deviation22945.64219
Coefficient of variation (CV)0.4397537513
Kurtosis-0.6693197982
Mean52178.38875
Median Absolute Deviation (MAD)14574
Skewness-0.422617849
Sum7768892479
Variance526502495.4
MonotonicityNot monotonic
2022-07-24T13:21:05.366307image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
605095221
 
3.5%
603824190
 
2.8%
659633883
 
2.6%
368043060
 
2.1%
613672566
 
1.7%
707482244
 
1.5%
687202126
 
1.4%
384452082
 
1.4%
358322051
 
1.4%
690431996
 
1.3%
Other values (544)119472
80.2%
ValueCountFrequency (%)
10373
 
< 0.1%
10671
 
< 0.1%
108134
< 0.1%
109619
< 0.1%
11301
 
< 0.1%
11491
 
< 0.1%
11514
 
< 0.1%
12893
 
< 0.1%
13156
 
< 0.1%
133915
< 0.1%
ValueCountFrequency (%)
11822097
 
0.1%
110724104
 
0.1%
10301872
 
< 0.1%
102785112
 
0.1%
100223146
0.1%
9948666
 
< 0.1%
98898118
 
0.1%
98320303
0.2%
95683152
0.1%
95254358
0.2%

Unique buyers
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct557
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37852.82088
Minimum577
Maximum79446
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-07-24T13:21:05.445989image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum577
5-th percentile4616
Q123674
median44835
Q350365
95-th percentile59954
Maximum79446
Range78869
Interquartile range (IQR)26691

Descriptive statistics

Standard deviation16908.08987
Coefficient of variation (CV)0.4466797843
Kurtosis-0.773944961
Mean37852.82088
Median Absolute Deviation (MAD)10306
Skewness-0.5136110297
Sum5635944354
Variance285883502.9
MonotonicityNot monotonic
2022-07-24T13:21:05.526317image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
452045221
 
3.5%
448354190
 
2.8%
490593883
 
2.6%
287703060
 
2.1%
472222566
 
1.7%
520782244
 
1.5%
514712126
 
1.4%
304562082
 
1.4%
247352051
 
1.4%
543901996
 
1.3%
Other values (547)119472
80.2%
ValueCountFrequency (%)
57734
< 0.1%
62719
< 0.1%
6553
 
< 0.1%
6845
 
< 0.1%
6971
 
< 0.1%
7031
 
< 0.1%
71626
< 0.1%
7436
 
< 0.1%
7896
 
< 0.1%
7923
 
< 0.1%
ValueCountFrequency (%)
7944697
 
0.1%
74144104
 
0.1%
73217112
 
0.1%
70524146
0.1%
69456118
 
0.1%
6939366
 
< 0.1%
69111157
0.1%
69023303
0.2%
68741152
0.1%
67857358
0.2%

Unique sellers
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct553
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25258.35159
Minimum487
Maximum69312
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-07-24T13:21:06.458716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum487
5-th percentile2895
Q115372
median28886
Q333080
95-th percentile43399
Maximum69312
Range68825
Interquartile range (IQR)17708

Descriptive statistics

Standard deviation11984.52378
Coefficient of variation (CV)0.4744776691
Kurtosis-0.649607659
Mean25258.35159
Median Absolute Deviation (MAD)8482
Skewness-0.1756541047
Sum3760741226
Variance143628810.3
MonotonicityNot monotonic
2022-07-24T13:21:06.534326image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300325221
 
3.5%
308364190
 
2.8%
325513883
 
2.6%
153743060
 
2.1%
296702566
 
1.7%
349902244
 
1.5%
330802126
 
1.4%
153722082
 
1.4%
160242051
 
1.4%
297861996
 
1.3%
Other values (543)119472
80.2%
ValueCountFrequency (%)
4873
 
< 0.1%
5121
 
< 0.1%
54519
 
< 0.1%
54935
< 0.1%
5541
 
< 0.1%
5774
 
< 0.1%
58015
 
< 0.1%
5926
 
< 0.1%
59351
< 0.1%
60212
 
< 0.1%
ValueCountFrequency (%)
6931297
 
0.1%
59638104
 
0.1%
5786772
 
< 0.1%
55134112
 
0.1%
5461066
 
< 0.1%
53456146
0.1%
53003134
0.1%
52468261
0.2%
51360303
0.2%
50975118
 
0.1%

Active market wallets collection
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct425
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1496.122116
Minimum8
Maximum5213
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-07-24T13:21:06.612498image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile202
Q1488
median1148
Q32020
95-th percentile3838
Maximum5213
Range5205
Interquartile range (IQR)1532

Descriptive statistics

Standard deviation1267.08867
Coefficient of variation (CV)0.8469152725
Kurtosis0.7293214092
Mean1496.122116
Median Absolute Deviation (MAD)716
Skewness1.172162902
Sum222759118
Variance1605513.697
MonotonicityNot monotonic
2022-07-24T13:21:06.691721image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52135221
 
3.5%
35134190
 
2.8%
38383883
 
2.6%
30693060
 
2.1%
30872566
 
1.7%
34712244
 
1.5%
23362126
 
1.4%
37162082
 
1.4%
20242051
 
1.4%
30851996
 
1.3%
Other values (415)119472
80.2%
ValueCountFrequency (%)
81
 
< 0.1%
111
 
< 0.1%
124
< 0.1%
135
< 0.1%
143
 
< 0.1%
167
< 0.1%
179
< 0.1%
181
 
< 0.1%
197
< 0.1%
204
< 0.1%
ValueCountFrequency (%)
52135221
3.5%
38383883
2.6%
37162082
 
1.4%
35134190
2.8%
34712244
1.5%
30872566
1.7%
30851996
 
1.3%
30693060
2.1%
29491779
 
1.2%
28981884
 
1.3%

Unique buyers collection
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct356
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1045.847257
Minimum4
Maximum3796
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-07-24T13:21:06.768263image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile99
Q1259
median783
Q31546
95-th percentile2670
Maximum3796
Range3792
Interquartile range (IQR)1287

Descriptive statistics

Standard deviation938.1294245
Coefficient of variation (CV)0.8970042402
Kurtosis0.7235530186
Mean1045.847257
Median Absolute Deviation (MAD)584
Skewness1.122327528
Sum155717244
Variance880086.8171
MonotonicityNot monotonic
2022-07-24T13:21:06.846866image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37965221
 
3.5%
18934190
 
2.8%
22263883
 
2.6%
22433060
 
2.1%
21492566
 
1.7%
26702244
 
1.5%
12652126
 
1.4%
32222082
 
1.4%
8902051
 
1.4%
22521996
 
1.3%
Other values (346)119472
80.2%
ValueCountFrequency (%)
41
 
< 0.1%
63
 
< 0.1%
79
< 0.1%
81
 
< 0.1%
94
 
< 0.1%
106
 
< 0.1%
1112
< 0.1%
127
< 0.1%
1315
< 0.1%
1413
< 0.1%
ValueCountFrequency (%)
37965221
3.5%
32222082
 
1.4%
26702244
1.5%
22591114
 
0.7%
22521996
 
1.3%
22433060
2.1%
22401779
 
1.2%
22281884
 
1.3%
22263883
2.6%
21771375
 
0.9%

Unique sellers collection
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct334
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean738.8569087
Minimum4
Maximum2777
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-07-24T13:21:06.924576image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile100
Q1237
median515
Q3898
95-th percentile2211
Maximum2777
Range2773
Interquartile range (IQR)661

Descriptive statistics

Standard deviation681.0736796
Coefficient of variation (CV)0.9217937486
Kurtosis1.306115254
Mean738.8569087
Median Absolute Deviation (MAD)314
Skewness1.415558866
Sum110009144
Variance463861.357
MonotonicityNot monotonic
2022-07-24T13:21:06.995546image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27775221
 
3.5%
22114190
 
2.8%
21503883
 
2.6%
16433060
 
2.1%
16452566
 
1.7%
16132244
 
1.5%
14022126
 
1.4%
12492082
 
1.4%
13192051
 
1.4%
14531996
 
1.3%
Other values (324)119472
80.2%
ValueCountFrequency (%)
42
 
< 0.1%
514
 
< 0.1%
68
 
< 0.1%
743
< 0.1%
813
 
< 0.1%
923
 
< 0.1%
1027
< 0.1%
1223
 
< 0.1%
138
 
< 0.1%
1466
< 0.1%
ValueCountFrequency (%)
27775221
3.5%
22114190
2.8%
21503883
2.6%
16452566
1.7%
16433060
2.1%
16132244
1.5%
14531996
 
1.3%
14022126
1.4%
13651884
 
1.3%
13192051
 
1.4%

tokens_minted
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct319
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean782.2986816
Minimum0
Maximum6184
Zeros1346
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-07-24T13:21:07.070937image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q159
median420
Q31100
95-th percentile2003
Maximum6184
Range6184
Interquartile range (IQR)1041

Descriptive statistics

Standard deviation1007.775024
Coefficient of variation (CV)1.288222833
Kurtosis11.2883619
Mean782.2986816
Median Absolute Deviation (MAD)416
Skewness2.795560059
Sum116477233
Variance1015610.499
MonotonicityNot monotonic
2022-07-24T13:21:07.144127image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20035221
 
3.5%
35107
 
3.4%
24456
 
3.0%
17823060
 
2.1%
162683
 
1.8%
10022624
 
1.8%
14242566
 
1.7%
20042244
 
1.5%
61842082
 
1.4%
4012051
 
1.4%
Other values (309)116797
78.4%
ValueCountFrequency (%)
01346
 
0.9%
1870
 
0.6%
24456
3.0%
35107
3.4%
41780
 
1.2%
5162
 
0.1%
61208
 
0.8%
81119
 
0.8%
9259
 
0.2%
107
 
< 0.1%
ValueCountFrequency (%)
61842082
 
1.4%
48251375
 
0.9%
236351
 
< 0.1%
2092593
 
0.4%
2027704
 
0.5%
20042244
1.5%
20035221
3.5%
1996682
 
0.5%
1952983
 
0.7%
18821449
 
1.0%

tokens_available
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct543
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111877.0624
Minimum5761
Maximum188401
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-07-24T13:21:07.222932image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum5761
5-th percentile38812
Q185519
median103345
Q3150309
95-th percentile181611
Maximum188401
Range182640
Interquartile range (IQR)64790

Descriptive statistics

Standard deviation43830.55423
Coefficient of variation (CV)0.3917742681
Kurtosis-0.7759447649
Mean111877.0624
Median Absolute Deviation (MAD)34666
Skewness-0.03372814277
Sum1.665748769 × 1010
Variance1921117484
MonotonicityNot monotonic
2022-07-24T13:21:07.299318image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
974085221
 
3.5%
954054190
 
2.8%
974113883
 
2.6%
764583060
 
2.1%
954032566
 
1.7%
1033452244
 
1.5%
989522126
 
1.4%
826422082
 
1.4%
1802092051
 
1.4%
999541996
 
1.3%
Other values (533)119472
80.2%
ValueCountFrequency (%)
576119
< 0.1%
58426
 
< 0.1%
590519
< 0.1%
59307
 
< 0.1%
604826
< 0.1%
611810
 
< 0.1%
64067
 
< 0.1%
66122
 
< 0.1%
67201
 
< 0.1%
67307
 
< 0.1%
ValueCountFrequency (%)
188401111
 
0.1%
188154409
0.3%
187634251
0.2%
187528271
0.2%
18710899
 
0.1%
187042175
0.1%
186967284
0.2%
186900282
0.2%
186798360
0.2%
186721303
0.2%

Action
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
Sale
148891 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSale
2nd rowSale
3rd rowSale
4th rowSale
5th rowSale

Common Values

ValueCountFrequency (%)
Sale148891
100.0%

Length

2022-07-24T13:21:07.370575image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-07-24T13:21:07.415700image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
sale148891
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Transactions collection ETH
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct560
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3173.326653
Minimum0.05512506263
Maximum16719.48346
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-07-24T13:21:07.461435image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.05512506263
5-th percentile55.72852333
Q1228.439
median838.0532781
Q34506.955602
95-th percentile11744.37427
Maximum16719.48346
Range16719.42834
Interquartile range (IQR)4278.516602

Descriptive statistics

Standard deviation4555.940221
Coefficient of variation (CV)1.43569847
Kurtosis1.669612544
Mean3173.326653
Median Absolute Deviation (MAD)752.2179781
Skewness1.666182274
Sum472479778.8
Variance20756591.29
MonotonicityNot monotonic
2022-07-24T13:21:07.536261image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16719.483465221
 
3.5%
10149.039864190
 
2.8%
11744.374273883
 
2.6%
5117.3210673060
 
2.1%
11410.36182566
 
1.7%
11171.686042244
 
1.5%
10493.703432126
 
1.4%
1945.847442082
 
1.4%
3145.7051632051
 
1.4%
16124.147911996
 
1.3%
Other values (550)119472
80.2%
ValueCountFrequency (%)
0.055125062631
 
< 0.1%
0.092996339881
 
< 0.1%
0.15626190711
 
< 0.1%
0.252
 
< 0.1%
0.34277940374
< 0.1%
0.52
 
< 0.1%
0.57540380647
< 0.1%
0.57861305556
< 0.1%
0.61411748386
< 0.1%
0.6263
< 0.1%
ValueCountFrequency (%)
16719.483465221
3.5%
16124.147911996
 
1.3%
11744.374273883
2.6%
11410.36182566
1.7%
11171.686042244
1.5%
10493.703432126
1.4%
10149.039864190
2.8%
9361.7100171884
 
1.3%
7464.9739461779
 
1.2%
7378.5200181061
 
0.7%

Number of Transactions collection
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct332
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1072.585878
Minimum1
Maximum5196
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-07-24T13:21:07.612658image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile78
Q1233
median593
Q31189
95-th percentile4178
Maximum5196
Range5195
Interquartile range (IQR)956

Descriptive statistics

Standard deviation1279.891692
Coefficient of variation (CV)1.193276658
Kurtosis2.632936724
Mean1072.585878
Median Absolute Deviation (MAD)422
Skewness1.839979763
Sum159698384
Variance1638122.743
MonotonicityNot monotonic
2022-07-24T13:21:07.698707image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51965221
 
3.5%
41784190
 
2.8%
38793883
 
2.6%
30413060
 
2.1%
25642566
 
1.7%
22382244
 
1.5%
21122126
 
1.4%
20692082
 
1.4%
14882051
 
1.4%
19921996
 
1.3%
Other values (322)119472
80.2%
ValueCountFrequency (%)
15
 
< 0.1%
22
 
< 0.1%
39
 
< 0.1%
416
 
< 0.1%
521
 
< 0.1%
618
 
< 0.1%
792
0.1%
816
 
< 0.1%
99
 
< 0.1%
1010
 
< 0.1%
ValueCountFrequency (%)
51965221
3.5%
41784190
2.8%
38793883
2.6%
30413060
2.1%
25642566
1.7%
22382244
1.5%
21122126
1.4%
20692082
 
1.4%
19921996
 
1.3%
18741884
 
1.3%

Transactions collection USD
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct561
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10049478.45
Minimum59.13040356
Maximum55496267.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-07-24T13:21:07.775052image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum59.13040356
5-th percentile130332.4347
Q1604121.2175
median2699537.293
Q313109819.47
95-th percentile37258514.13
Maximum55496267.94
Range55496208.81
Interquartile range (IQR)12505698.25

Descriptive statistics

Standard deviation14863629.43
Coefficient of variation (CV)1.479044858
Kurtosis1.964928242
Mean10049478.45
Median Absolute Deviation (MAD)2383357.695
Skewness1.735461834
Sum1.496276896 × 1012
Variance2.209274798 × 1014
MonotonicityNot monotonic
2022-07-24T13:21:07.847855image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55496267.945221
 
3.5%
32904359.234190
 
2.8%
37258514.133883
 
2.6%
13109819.473060
 
2.1%
36810785.472566
 
1.7%
36021697.012244
 
1.5%
32533895.72126
 
1.4%
5078960.1552082
 
1.4%
8292371.4172051
 
1.4%
52735651.551996
 
1.3%
Other values (551)119472
80.2%
ValueCountFrequency (%)
59.130403561
 
< 0.1%
67.565582081
 
< 0.1%
158.71354681
 
< 0.1%
162.54879751
 
< 0.1%
233.99573224
< 0.1%
301.77804571
 
< 0.1%
304.90893552
 
< 0.1%
359.87970466
< 0.1%
366.06144317
< 0.1%
422.61743616
< 0.1%
ValueCountFrequency (%)
55496267.945221
3.5%
52735651.551996
 
1.3%
37258514.133883
2.6%
36810785.472566
1.7%
36021697.012244
1.5%
32904359.234190
2.8%
32533895.72126
1.4%
30373163.061884
 
1.3%
24073908.561779
 
1.2%
23108784.261041
 
0.7%

trait_type
Categorical

HIGH CARDINALITY

Distinct302
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
None
 
11284
Flowers
 
6213
Transitions
 
4119
Paper Armada
 
2495
Cryptoblots
 
2353
Other values (297)
122427 

Length

Max length41
Median length10
Mean length10.96100503
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowCENTURY
2nd rowCENTURY
3rd rowCENTURY
4th rowCENTURY
5th rowCENTURY

Common Values

ValueCountFrequency (%)
None11284
 
7.6%
Flowers6213
 
4.2%
Transitions4119
 
2.8%
Paper Armada2495
 
1.7%
Cryptoblots2353
 
1.6%
Scoundrels2056
 
1.4%
Apparitions1713
 
1.2%
Inspirals1493
 
1.0%
phase1449
 
1.0%
Kai-Gen1430
 
1.0%
Other values (292)114286
76.8%

Length

2022-07-24T13:21:07.929502image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
none11284
 
4.8%
of6407
 
2.7%
flowers6213
 
2.7%
transitions4119
 
1.8%
paper2495
 
1.1%
armada2495
 
1.1%
cryptoblots2353
 
1.0%
2089
 
0.9%
scoundrels2056
 
0.9%
generator2048
 
0.9%
Other values (423)192664
82.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

rarity_trait_value
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct38089
Distinct (%)25.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06443989781
Minimum0.0002340898302
Maximum0.3870420898
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-07-24T13:21:08.006976image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.0002340898302
5-th percentile0.007684253123
Q10.02033528177
median0.02918998405
Q30.04485364704
95-th percentile0.3870420898
Maximum0.3870420898
Range0.3868079999
Interquartile range (IQR)0.02451836526

Descriptive statistics

Standard deviation0.09877635928
Coefficient of variation (CV)1.532844754
Kurtosis5.705454189
Mean0.06443989781
Median Absolute Deviation (MAD)0.01082029351
Skewness2.634567539
Sum9594.520825
Variance0.009756769153
MonotonicityNot monotonic
2022-07-24T13:21:08.085998image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.387042089811284
 
7.6%
0.05781815251886
 
0.6%
0.05868530266770
 
0.5%
0.02265582444363
 
0.2%
0.05844103501318
 
0.2%
0.03798361941309
 
0.2%
0.01221338245279
 
0.2%
0.02132456575268
 
0.2%
0.04420862778259
 
0.2%
0.02064061634253
 
0.2%
Other values (38079)133902
89.9%
ValueCountFrequency (%)
0.00023408983022
< 0.1%
0.00024426764891
< 0.1%
0.00043968176811
< 0.1%
0.00046817966051
< 0.1%
0.00050889093531
< 0.1%
0.00052924657272
< 0.1%
0.00053942439141
< 0.1%
0.00054960221012
< 0.1%
0.00056995784752
< 0.1%
0.000576743062
< 0.1%
ValueCountFrequency (%)
0.387042089811284
7.6%
0.187824374498
 
0.1%
0.1875801067100
 
0.1%
0.186193015484
 
0.1%
0.184631447292
 
0.1%
0.184387179677
 
0.1%
0.183000088356
 
< 0.1%
0.181682787732
 
< 0.1%
0.18060103162
 
< 0.1%
0.180356763466
 
< 0.1%

number_trait_values
Real number (ℝ≥0)

ZEROS

Distinct39
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.704810902
Minimum0
Maximum69
Zeros7874
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-07-24T13:21:08.164005image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median9
Q314
95-th percentile19
Maximum69
Range69
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.457571638
Coefficient of variation (CV)0.7684406954
Kurtosis24.15141626
Mean9.704810902
Median Absolute Deviation (MAD)4
Skewness3.613137148
Sum1444959
Variance55.61537473
MonotonicityNot monotonic
2022-07-24T13:21:08.232757image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
516245
 
10.9%
712841
 
8.6%
1411444
 
7.7%
810150
 
6.8%
910012
 
6.7%
109961
 
6.7%
69643
 
6.5%
129060
 
6.1%
07874
 
5.3%
115909
 
4.0%
Other values (29)45752
30.7%
ValueCountFrequency (%)
07874
5.3%
15575
 
3.7%
21593
 
1.1%
34227
 
2.8%
44624
 
3.1%
516245
10.9%
69643
6.5%
712841
8.6%
810150
6.8%
910012
6.7%
ValueCountFrequency (%)
697
 
< 0.1%
6825
 
< 0.1%
67107
 
0.1%
66199
0.1%
65404
0.3%
64281
0.2%
63213
0.1%
6283
 
0.1%
619
 
< 0.1%
4539
 
< 0.1%

rarity_trait_type
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct224
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9509810272
Minimum0.002383591357
Maximum3.776800505
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2022-07-24T13:21:08.307101image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.002383591357
5-th percentile0.1525498469
Q10.3873335955
median0.5655070495
Q30.5958978393
95-th percentile3.776800505
Maximum3.776800505
Range3.774416914
Interquartile range (IQR)0.2085642437

Descriptive statistics

Standard deviation1.077587175
Coefficient of variation (CV)1.133132149
Kurtosis1.967387566
Mean0.9509810272
Median Absolute Deviation (MAD)0.1448031749
Skewness1.879495005
Sum141592.5161
Variance1.16119412
MonotonicityNot monotonic
2022-07-24T13:21:08.382504image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.77680050511284
 
7.6%
3.4234330876213
 
4.2%
0.59589783935569
 
3.7%
0.570870134135
 
2.8%
2.6261217784119
 
2.8%
0.56848653873381
 
2.3%
0.29794891962887
 
1.9%
0.56252756032814
 
1.9%
0.57146602792785
 
1.9%
0.57027423222521
 
1.7%
Other values (214)103183
69.3%
ValueCountFrequency (%)
0.0023835913571
 
< 0.1%
0.0041712848752
 
< 0.1%
0.0053630805532
 
< 0.1%
0.011322058954
 
< 0.1%
0.011917956794
 
< 0.1%
0.0131097524613
< 0.1%
0.014301548142
 
< 0.1%
0.0172810373412
< 0.1%
0.0178769351820
< 0.1%
0.020260526541
 
< 0.1%
ValueCountFrequency (%)
3.77680050511284
7.6%
3.4234330876213
4.2%
2.6261217784119
 
2.8%
1.7078432072495
 
1.7%
1.1917956791042
 
0.7%
1.190603883712
 
0.5%
1.1578295022056
 
1.4%
1.1465074431430
 
1.0%
1.1447197492353
 
1.6%
0.89384675891713
 
1.2%

Interactions

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2022-07-24T13:20:45.912886image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-24T13:20:48.063945image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-24T13:20:50.236745image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-24T13:20:52.875754image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-24T13:20:54.970791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-24T13:20:57.133155image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-24T13:20:59.311481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-24T13:21:01.636664image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-24T13:20:08.648177image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-24T13:20:10.762710image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-24T13:20:12.941558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-24T13:20:15.011441image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-24T13:20:17.359069image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-24T13:20:19.452927image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-24T13:20:21.590980image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-24T13:20:23.948109image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-24T13:20:26.040002image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-24T13:20:28.131904image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-24T13:20:30.278571image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-24T13:20:32.724352image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-24T13:20:34.902922image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-24T13:20:37.072163image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-24T13:20:39.213057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-24T13:20:41.791546image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-24T13:20:43.890307image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-24T13:20:46.003202image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-24T13:20:48.154296image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-24T13:20:50.324028image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-24T13:20:52.962070image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-24T13:20:55.055334image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-24T13:20:57.220297image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-07-24T13:20:59.406818image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-07-24T13:21:08.483815image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-07-24T13:21:08.666251image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-07-24T13:21:08.845476image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-07-24T13:21:09.020841image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-07-24T13:21:01.839001image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-07-24T13:21:02.586541image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

blockNumberDATETIMEcollectionTokenIdpricegasPricegasDATEETH_CLOSE_PRICEETH_TRADED_VOLUMEINTEREST_SCORE_NFTINTEREST_SCORE_ARTBLOCKSNumber of salesSales USDActive market walletsUnique buyersUnique sellersActive market wallets collectionUnique buyers collectionUnique sellers collectiontokens_mintedtokens_availableActionTransactions collection ETHNumber of Transactions collectionTransactions collection USDtrait_typerarity_trait_valuenumber_trait_valuesrarity_trait_type
0141115962022-01-31 04:52:12+00:001000000014.009.552986e-083.108360e-132022-01-312688.2788091377823461450.037.0103012.02.025274e+0878557.053131.039627.01122.01007.0462.01025.0158454.0Sale935.176948490.02.514016e+06CENTURY0.02339313.00.55776
1131486932021-09-02 21:21:35+00:001000000027.501.156654e-073.468240e-132021-09-023790.9899902438739733034.585.0129782.01.601383e+0855908.041108.025805.0741.0371.0407.03.0104653.0Sale2039.309127508.07.731000e+06CENTURY0.02445013.00.55776
2132613042021-09-20 07:37:21+00:001000000026.906.772837e-083.106900e-132021-09-202958.9934082737168458137.061.0154527.07.160406e+0760894.045261.029150.0342.0153.0199.01.0112713.0Sale759.752406171.02.248102e+06CENTURY0.02445013.00.55776
3129242752021-07-30 02:00:33+00:001000000031.503.700000e-082.349380e-132021-07-302466.9614262021284893423.031.0109497.07.911870e+0735131.028570.013827.01488.01284.0575.01172.069851.0Sale888.028976650.02.190733e+06CENTURY0.03009213.00.55776
4129234112021-07-29 22:42:14+00:001000000041.603.490000e-082.351920e-132021-07-292380.9567871631337311323.031.0109354.05.419637e+0735868.028562.014730.0610.0288.0392.0102.068679.0Sale1296.823140514.03.087680e+06CENTURY0.02559213.00.55776
5129233702021-07-29 22:29:02+00:001000000051.602.800000e-082.349250e-132021-07-292380.9567871631337311323.031.0109354.05.419637e+0735868.028562.014730.0610.0288.0392.0102.068679.0Sale1296.823140514.03.087680e+06CENTURY0.02415913.00.55776
6131335682021-08-31 13:27:45+00:0010000000614.007.407684e-082.937130e-132021-08-313433.7326662728050298734.585.0177071.01.870942e+0865770.048929.032377.01520.0732.0890.0101.0103446.0Sale5293.0934321047.01.817507e+07CENTURY0.02976313.00.55776
7127116462021-06-26 20:00:40+00:001000000070.792.500000e-082.349250e-132021-06-261829.2392582063754236123.533.033471.01.352912e+0716530.011733.08022.0514.0316.0267.0804.049750.0Sale190.056746295.03.476593e+05CENTURY0.02868313.00.55776
8129276412021-07-30 14:58:56+00:001000000071.503.900000e-082.697100e-132021-07-302466.9614262021284893423.031.0109497.07.911870e+0735131.028570.013827.01488.01284.0575.01172.069851.0Sale888.028976650.02.190733e+06CENTURY0.02868313.00.55776
9127161182021-06-27 12:43:01+00:001000000090.791.300000e-082.349250e-132021-06-271978.8946531988547474222.522.059676.01.593843e+0718402.014067.08562.0519.0355.0234.0454.050204.0Sale203.950064239.04.035957e+05CENTURY0.02872513.00.55776

Last rows

blockNumberDATETIMEcollectionTokenIdpricegasPricegasDATEETH_CLOSE_PRICEETH_TRADED_VOLUMEINTEREST_SCORE_NFTINTEREST_SCORE_ARTBLOCKSNumber of salesSales USDActive market walletsUnique buyersUnique sellersActive market wallets collectionUnique buyers collectionUnique sellers collectiontokens_mintedtokens_availableActionTransactions collection ETHNumber of Transactions collectionTransactions collection USDtrait_typerarity_trait_valuenumber_trait_valuesrarity_trait_type
148881124759522021-05-21 05:55:05+00:00200000450.184.524000e-082.349500e-132021-05-212430.6213385377407080256.024.09525.04.828519e+066055.04141.02836.0271.0214.071.0287.039259.0Sale32.74970750.07.960214e+04Sentience0.0064365.00.085809
148882129008172021-07-26 08:38:03+00:00200000451.502.400000e-082.349900e-132021-07-262233.3666992961432423323.031.066001.03.957673e+0727248.021584.010817.01254.0783.0680.01096.066104.0Sale2127.872455927.04.752319e+06Sentience0.0064365.00.085809
148883137091082021-11-29 14:23:54+00:00200000450.801.528107e-072.773720e-132021-11-294445.1049801908647583734.567.0168095.01.224729e+0884055.059833.042588.01253.01099.0529.01047.0141415.0Sale738.431392573.03.282405e+06Sentience0.0064365.00.085809
148884119549552021-03-01 22:08:52+00:00200000470.959.317000e-082.590890e-132021-03-011564.7076422403283864521.539.013635.01.015667e+075224.03543.02176.0385.0335.0114.0369.022842.0Sale327.699743173.05.127543e+05Sentience0.0168185.00.085809
148885128602202021-07-20 00:19:41+00:00200000480.802.400000e-082.349500e-132021-07-201787.5107421736859763628.018.064717.02.573770e+0730639.023217.012119.0485.0242.0274.0132.062543.0Sale284.427000263.05.084163e+05Sentience0.0064365.00.085809
148886122483742021-04-16 02:10:04+00:00200000490.531.300000e-072.522210e-132021-04-162431.9465333619692825627.541.05397.01.282548e+074168.02853.01685.0503.0475.0105.0606.034070.0Sale52.98870094.01.288657e+05Sentience0.0069255.00.085809
148887131506942021-09-03 04:55:35+00:00200000494.001.195131e-073.431990e-132021-09-033940.6147462620776509434.585.0134408.01.604993e+0858013.042631.027845.01690.01411.0578.01002.0105655.0Sale3013.463165628.01.187490e+07Sentience0.0069255.00.085809
148888127242362021-06-28 18:55:37+00:00200000501.502.100000e-082.349500e-132021-06-282079.6574712551460284122.522.035194.01.457666e+0717746.012761.08703.0904.0581.0438.0931.051135.0Sale299.183344679.06.221989e+05Sentience0.0049715.00.085809
148889118662122021-02-16 06:32:59+00:00200000530.351.130000e-072.157160e-132021-02-161781.0675053426936926844.042.04738.08.908437e+063359.02063.01658.0173.0120.078.0186.017663.0Sale61.700139116.01.098921e+05Sentience0.0164645.00.085809
148890130690372021-08-21 13:56:24+00:00200000543.002.606906e-083.026760e-132021-08-213226.0839841811397762834.066.0172471.01.455080e+0861367.047222.029670.03087.02149.01645.01424.095403.0Sale11410.3618042564.03.681079e+07Sentience0.0164645.00.085809

Duplicate rows

Most frequently occurring

blockNumberDATETIMEcollectionTokenIdpricegasPricegasDATEETH_CLOSE_PRICEETH_TRADED_VOLUMEINTEREST_SCORE_NFTINTEREST_SCORE_ARTBLOCKSNumber of salesSales USDActive market walletsUnique buyersUnique sellersActive market wallets collectionUnique buyers collectionUnique sellers collectiontokens_mintedtokens_availableActionTransactions collection ETHNumber of Transactions collectionTransactions collection USDtrait_typerarity_trait_valuenumber_trait_valuesrarity_trait_type# duplicates
26144565562022-03-25 16:38:31+00:002810001890.2908.216512e-083.292200e-132022-03-253106.6713871703050383117.031.048431.066293113.8137223.023373.020501.0605.0480.0320.0517.0171679.0Sale479.500049367.01.489649e+06Automatism0.0126009.00.2425304
27144566402022-03-25 16:56:24+00:002810001910.2507.723285e-083.208470e-132022-03-253106.6713871703050383117.031.048431.066293113.8137223.023373.020501.0605.0480.0320.0517.0171679.0Sale479.500049367.01.489649e+06Automatism0.0140259.00.2425304
28144566542022-03-25 17:00:04+00:002810001900.1908.034550e-083.490150e-132022-03-253106.6713871703050383117.031.048431.066293113.8137223.023373.020501.0605.0480.0320.0517.0171679.0Sale479.500049367.01.489649e+06Automatism0.0112439.00.2425304
29144573342022-03-25 19:32:35+00:002810001940.3006.331367e-083.292270e-132022-03-253106.6713871703050383117.031.048431.066293113.8137223.023373.020501.0605.0480.0320.0517.0171679.0Sale479.500049367.01.489649e+06Automatism0.0139719.00.2425304
30144579102022-03-25 21:47:00+00:002810001900.5004.458339e-083.239890e-132022-03-253106.6713871703050383117.031.048431.066293113.8137223.023373.020501.0605.0480.0320.0517.0171679.0Sale479.500049367.01.489649e+06Automatism0.0112439.00.2425304
32144582762022-03-25 23:11:22+00:002810001891.3001.011839e-073.209000e-132022-03-253106.6713871703050383117.031.048431.066293113.8137223.023373.020501.0605.0480.0320.0517.0171679.0Sale479.500049367.01.489649e+06Automatism0.0126009.00.2425304
116145896832022-04-15 11:31:01+00:002830018490.0743.669601e-083.676660e-132022-04-153040.9165041125665153631.537.037222.048452972.2232201.019497.017034.0883.0758.0283.01511.0177269.0Sale156.719347354.04.765704e+05OnChainChain0.0622124.01.1906044
117145897712022-04-15 11:49:53+00:002830018430.0792.739472e-083.034190e-132022-04-153040.9165041125665153631.537.037222.048452972.2232201.019497.017034.0883.0758.0283.01511.0177269.0Sale156.719347354.04.765704e+05OnChainChain0.0719834.01.1906044
120145902162022-04-15 13:28:43+00:002830018440.0803.217039e-083.033880e-132022-04-153040.9165041125665153631.537.037222.048452972.2232201.019497.017034.0883.0758.0283.01511.0177269.0Sale156.719347354.04.765704e+05OnChainChain0.0722734.01.1906044
121145906612022-04-15 15:07:20+00:002830018500.1005.699079e-083.292200e-132022-04-153040.9165041125665153631.537.037222.048452972.2232201.019497.017034.0883.0758.0283.01511.0177269.0Sale156.719347354.04.765704e+05OnChainChain0.0617394.01.1906044